Implement an iterative algorithm (k-means) in Spark to calculate k-means fora set of points that are in a file, a k-means algorithm in python. Do not use use K-means in MLib of Spark to solve the problem. Set the center points to k=5. Follow this pattern: Randomly assign a centroid to each of the k clusters (k =5). Calculate the distance of all observation to each of the k centroids Assign observations to the closest centroid Find the new location of the centroid by taking the mean of all the observations in each cluster Repeat steps 3-5 until the centroids do not change position Note: You need a variable to decide when the K-means calculation is done – whenthe amount the locations of the means changes between iterations is less than the variable. Setthe variable = 0.1. Example of imput file (an rdd): [(7869, 8696), (8676, -4746), (9484, 112526), (-1827, 5958), (987, 900087), (18127, 9383), (298, 272), (91716, 2827), (12625, 92827) ........]

It is straightforward and efficient to compute the union of two sets using Boolean values. We may create a new union set by Oring the matching items of the two BitArrays since the union of two sets is a combination of the members of both sets. At other words, if the value in the corresponding place of either BitArray is True, a member is added to the new set.Computing the intersection of two sets is analogous to computing the union, except that the And operator is used instead of the Or operator Using the same technique we used to detect the difference, we can determine if one set is a subset of another. For example, if:setA(index) && !(setB(index))evaluates to False then setA is not a subset of setB.The BitArray Set ImplementationWrite The code for a CSet class based on a BitArray.

Given a data set x, the following R commands have been run: library(cluster); agnes (x=X)->AG; KPM Match the following objects with what you expect the R output to be. PM$id.med Choose... PM$clustering Choose... AG$order Choose... AG$ac Choose... AG$height Choose... [1] 1, 1, 2, 1, 1, 1, 2 [1] 1, 5, 6, 7, 2, 3, 4 [1] 6, 7 [1] 0.1517008 [1] 4, 4, 4, 4.7948, 4.4721, 4

IN PYTHON OPENCV We will create this vocabulary by randomly selecting tens or hundreds of thousands of local features from our training set and clustering them with k-means. The number of k-means clusters represents the size of our vocabulary and features. For example, you could begin by clustering a large number of SIFT descriptors into k=50 clusters. This divides the 128-dimensional continuous SIFT feature space into 50 regions. As long as we keep the centroids of our original clusters, we can figure out which region any new SIFT feature belongs to. Our visual word vocabulary is made up of centroids. Work with Historgrams. We will densely sample many SIFT descriptors for each image. Rather than storing hundreds of SIFT descriptors, we simply count the number of SIFT descriptors that fall into each cluster in our visual word vocabulary. This is accomplished by locating the nearest neighbor k-means centroid for each SIFT feature. Thus, if we have a visual vocabulary of 50 words and…